3D Point Cloud from Nakadake Sanroku Kiln Site Center, Japan: Sample Data for the Application of Adaptive Filtering with the AFwizard

Version 2.1
Shinoto, Maria; Doneus, Michael; Haijima, Hideyuki; Weiser, Hannah; Zahs, Vivien; Kempf, Dominic; Daskalakis, Gwydion; Höfle, Bernhard; Nakamura, Naoko, 2022, "3D Point Cloud from Nakadake Sanroku Kiln Site Center, Japan: Sample Data for the Application of Adaptive Filtering with the AFwizard", https://doi.org/10.11588/data/TJNQZG, heiDATA, V2
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Description
This data set represents 3D point clouds acquired with LiDAR technology and related files from a subregion of 150*436 sqm in the ancient Nakadake Sanroku Kiln Site Center in South Japan. It is a densely vegetated mountainous region with varied topography and vegetation. The data set contains the original point cloud (reduced from a density of 5477 points per square meter to 100 points per square meter), a segmentation of the area based on characteristics in vegetation and topography, and filter pipelines for segments with different characteristics, and other data necessary. The data serve to test the AFwizard software which can create a DTM from the point cloud with varying filter and filter parameter selections based on varying segment characteristics (https://github.com/ssciwr/afwizard).

The AFwizard adds flexibility to ground point filtering of 3D point clouds, which is a crucial step in a variety of applications of LiDAR technology. Digital Terrain Models (DTM) derived from filtered 3D point clouds serve various purposes and therefore, rather than creating one representation of the terrain that is supposed to be "true", a variety of models can be derived from the same point cloud according to the intended usage of the DTM.

The sample data were acquired during an archaeological research project in a mountainous and densely forested region in South Japan -- the Nakadake-Sanroku Kiln Site Center: LiDAR data were acquired in a subregion of 0.5 sqkm, a relatively small area characterized by frequent and sudden changes in topography and vegetation. The point cloud is very dense due to the technology chosen (UAV multicopter GLYPHON DYNAMICS GD-X8-SP; LiDAR scanner RIEGL VUX-1 UAV).

Usage of the data is restricted to the citation of the article mentioned below.

Version 2.01: 2023-05-11; Article citation updated; 2022-07-21; Documentation (HowTo - Minimal Workflow) updated, data files tagged.
Subject Arts and Humanities; Earth and Environmental Sciences
Keyword Japan, Adaptive 3D point cloud filtering, Digital Terrain Model (DTM), archaeological prospection, AFwizard
Related Publication Doneus, Michael, Bernhard Höfle, Dominic Kempf, Gwydion Daskalakis, and Maria Shinoto. “Human-in-the-Loop Development of Spatially Adaptive Ground Point Filtering Pipelines—An Archaeological Case Study.” Archaeological Prospection 29, no. 4 (2022): 503–24. doi: https://doi.org/10.1002/arp.1873
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CC BY-SA 4.0

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How To - Minimal Workflow.pdf
Adobe PDF - 1.6 MB
Published Jul 21, 2022
75 Downloads
MD5: 99c7b15060c5509dbb0a61d15263534a
Documentation
nkd_fpl_paddy_LT.json
JSON - 312 B
Published Jul 5, 2022
172 Downloads
MD5: a5835047e1a69987e02599d7fc1821c6
Filter pipeline for segments of class "paddy field".
working_data
nkd_fpl_slope_LT.json
JSON - 300 B
Published Jul 5, 2022
131 Downloads
MD5: 1fe264da9d9691837fb718f99cf96803
Filter pipeline for segments of class "slope" and "ridge".
working_data
nkd_fpl_valley_TF.json
JSON - 563 B
Published Jul 5, 2022
166 Downloads
MD5: 5f845e8634511fea8aa1cde9d3ad288f
Filter pipeline for segments of class "valley".
working_data
nkd_pcl_epsg6670.laz
Unknown - 77.7 MB
Published Jul 5, 2022
119 Downloads
MD5: e9b080ab871e5a96a31c73179235dd9e
Input data: 3D point cloud from the sample region to which the filters are applied with the AFwizard.
input_dataoriginal_data
nkd_sgm.geojson
GeoJSON - 26.4 KB
Published Jul 5, 2022
65 Downloads
MD5: f352d25ba4ca4139a94d478d0fdfa6a5
Input data: Segmentation of the sample area with segment classes but without filter pipelines assigned.
auxiliary_datainput_data
nkd_sgm_assigned_TF.geojson
GeoJSON - 18.4 KB
Published Jul 5, 2022
115 Downloads
MD5: cb34ffb4e3214f613330c72a9093c120
Segmentation of the sample area with segment classes and the adapted filter pipelines assigned.
working_data
sample_pcl_paddy.las
Unknown - 4.0 MB
Published Jul 5, 2022
88 Downloads
MD5: ecb287c259ef82ed4e449f718abf8abf
3D point cloud of a sub-area that represents segments of class "paddy".
working_data
sample_pcl_ridge.las
Unknown - 2.1 MB
Published Jul 5, 2022
64 Downloads
MD5: 2a372cd47af207796c29f06ce650f381
3D point cloud of a sub-area that represents segments of class "ridge".
working_data
sample_pcl_slope.las
Unknown - 2.0 MB
Published Jul 5, 2022
71 Downloads
MD5: 5a90a1674a763f4520c5ec166edb9f4f
3D point cloud of a sub-area that represents segments of class "slope".
working_data
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